The influence of the firms’ communicational
strategies (posts) on Facebook, to their band
Contents
•
Importance of the topic. 3
•
Conceptual model and hypotheses. 4
•
Method and measurements. 5
•
Results. 12
•
Conclusions and Implications 14
Importance of the topic
• Academic importance:
Most of the researches have a focus on the general benefits of social media
strategies to the (brand image, brand attitude, brand equity) There was a need for an in-depth analysis.
One research that dived in an in-depth analysis focused directly on the effects of social media strategies to brand equity Brand equity is the final goal and destination of all firms, there are also other stages should be analyzed before. (brand attitude).
• Managerial importance:
A positive online brand image, influences consumers’ decision making and
consumers’ patronage.
Small and medium-sized enterprises in Europe account for 99,8% of all enterprises 93,2% of these are micro SMEs (< 10 employees). For the micro SMEs social media marketing is their only way to build a positive brand image and brand attitude as they lack of marketing resources.
Method and measurements
•
Smart PLS (PLS-SEM) structural equation modeling. Why?• Is a very useful approach when it comes to predictions and explanations of target constructs.
• Measure the effects of each latent variable to others simultaneously in the hypothesized causal sequence. (No need for separate regressions).
• Contains all the statistical measures that indicate the validity and reliability of a model.
• Offers a better understanding and interpretation of the different effects between variables along the overall path
• Is an appropriate model for the generation of valid and reliable results when the samples are relatively small.
Method and measurements
• Traditional PLS algorithm – Traditional bootstrapping algorithm.
WHY?
• Mixed model consist from reflective measures and formative measures.
• The indicators of the brand attitude (e.g. pleasant idea, good reputation, positive characteristics) represent a different dimension of the latent variable (Attitude)
• The set of them collectively represents all the dimensions of the latent variable.
• If I drop one of these indicators I will also drop the meaning of my latent variable (Attitude). Thus the meaning of Attitude will change.
• In the case of mixed models the traditional algorithms of PLS should be implemented.
Factor loadings and VIF scores
• All loadings above 0.5 However information sharing indicator is relatively low. • VIF scores 4. 18 and less No worrying multicollinearity issues .
Malhotra (2010)
Factor loadings
Reliability and Validity 1/2 (reflective measure)
• After the deletion of the low loading variable the composite reliability increased (0.79 0.83). Thus the definitively deletion of this variable should be applied.
• Composite reliability for all latent variables is higher than 0.7
• Loadings of all indicators are higher than 0.7 to their latent variables.
• Average Variance Extracted for all latent variables is higher than 0.5 Convergent validity is satisfied.
Reliability and Validity 2/2 (reflective measure)
• The average variance extracted for each latent variable is indeed higher than the variable’s highest square correlations with any of the other latent variables.
Thus Fornell-Lacker criterion is also satisfied discriminant validity is satisfied.
Fornell-Lacker criterion
Contribution of indicators to the formative measured
variable (Attitude)
Outer weights
Outer Loadings
• All the indicators have significant weights to the latent variable Attitude. • All the indicators have higher than 0.5 significant loadings.
Thus all the indicators are able to provide empirical support of their relevance to the formative latent variable (Attitude).
R-squared and R-squared adjusted
• R-square for all latent variables is 0.2 and higher
• R-square adjusted for all latent variables is very close to R-square.
The indicators of the latent variables explain a lot of their variance.
The indicators that used for the latent variables do not have trivial correlations with the endogenous variables. Thus the penalty of the R-square is relatively small.
Results
H1a: Positive and significant H1a supported.
H1b: significant at the level of (0.1) H1b is not supported at the level of (0,1) H2a: not significant effect H2a supported.
H2b: Positive and significant effect H2b supported.
H3a: Positive and significant H3a supported at the level of (0,1) H3b: Positive and significant H3b Not supported.
Conclusions and Implications
•
Different types of communication strategies on Facebook can influence the
image of the brands differently.
•
Future researches that will focus on the benefits that social media offer to
the firms should take under consideration both aspects of the brand image
(Functional- Hedonic) and the various communication strategies of firms on
social media.
•
Managers and marketing practitioners should never underestimate the
importance of a favorable hedonic brand image to the creation of a positive
brand attitude.
References
• Da Silva, R. V., & Alwi, S. F. S. (2008). Online brand attributes and online corporate brand images. European Journal of Marketing. https://doi.org/10.1108/03090560810891136
• European Commission. (2018). Annual report on European SMEs 2017/2018: SMEs growing
beyond boarders. https://ec.europa.eu/growth/smes/business-friendly-environment/performance-review_en
• Garson, G. D. (2016). Partial Least Squares: Regression & Stractural equation models. Statistical associates publishing. pp13
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice. https://doi.org/10.2753/MTP1069-6679190202
• F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review. https://doi.org/10.1108/EBR-10-2013-0128
• Keller, K. L. (2013). Strategic Brand Management, Building, Measuring, and Managing Brand Equity Global Edition. Pearson Education, Inc. https://doi.org/10.2307/1252315
